Consider a case where you search for a gadget in Google Image search. Your screen will be deluged with numerous images of it drawn from infinite quarters across world wide web. The logic behind surfacing these images is quite simple. If the image uploaded has a name, title and'alt' tag mentioned, that image will get picked by google crawlers. Another possibility is, even though the image might not have any such parameters, but if the web page content(where images reside) matches the search term, the image in that web page shows up. This works fluently if it was not been abused by black strategies.

Investors poured a record high 1.05B into artificial intelligence startups in Q2'16, and AI is already affecting more areas of our lives than many people realize. Even retail and e-commerce companies are increasingly integrating the technology. Recently there's been a rush of AI announcements and acquisitions by major retailers: Just last week, Etsy acquired Blackbird to enhance its search functionality through AI, followed the very next day by Amazon acquiring Angel.ai And earlier this month, e-commerce unicorn Houzz (see our full unicorn tracker here) announced a deep learning initiative to help users find and buy products by clicking on images. Using CB Insights data, we dove into the wide array of AI startups focused on retailers and e-commerce businesses, including AI-powered personal shopping apps, natural language processing and image recognition tools for shopping websites, predictive inventory allocation tools, and more.

We haven't designed fully sentient artificial intelligence just yet, but we're steadily teaching computers how to see, read, and understand our world. Last month, Google engineers showed off their "Deep Dream," software capable of taking an image and ascertaining what was in it by turning it into a nightmare fusion of flesh and tentacles. The release follows research by scientists from Stanford University, who developed a similar program called NeuralTalk, capable of analyzing images and describing them with eerily accurate sentences. First published last year, the program and the accompanying study is the work of Fei-Fei Li, director of the Stanford Artificial Intelligence Laboratory, and Andrej Karpathy, a graduate student. Their software is capable of looking at pictures of complex scenes and identifying exactly what's happening.

The application of pattern recognition technology to large datasets has revolutionised the digital economy. But digital represents only 5% of GDP in OECD countries: the remaining 95% is still largely untouched by data science (DS). The larger "old economy" companies are just beginning their data journey and data science is yet to be institutionalised: Outside the tech leviathans DS is still a cottage industry with artisan DS crafting bespoke prototypes to their own standards. If DS is to fulfil its promise, it needs to industrialise. This blog explains what I mean by this, and proposes a number of issues which must be addressed if it is to do so. Most DS blogs are technical: algorithms, distributed computation, visualisation etc. The rest are case studies of projects where these techniques are applied to a domain.

Deep learning and GPU computing have quickly advanced the abilities of image recognition technology to superhuman levels. Now, PicsArt, maker of the social photo editor by the same name, is applying this breakthrough in artificial intelligence to the creation of images. Hitting the market today, "Magic Effects" is a new feature in the latest version of the PicsArt app, which has been downloaded more than 300 million times and boasts 80 million active monthly users. Magic Effects uses GPU-powered AI to analyze the quality and context of photos, and enables users to transform their pics in seconds with an array of filtering effects that are customized based on the AI analysis. If, for example, a user applies the "Neo Pop" effect to a photo, the result won't be standardized.

Abstract--Although highly correlated, speech and speaker recognition have been regarded as two independent tasks and studied by two communities. This is certainly not the way that people behave: we decipher both speech content and speaker traits at the same time. This paper presents a unified model to perform speech and speaker recognition simultaneously and altogether . The model is based on a unified neural network where the output of one task is fed to the input of the other, leading to a multi-task recurrent network. Experiments show that the joint model outperforms the task-specific models on both the two tasks.

Many scientists and futurists agree that the effects of artificial intelligence and automation on society are difficult to predict. While many predicted that the tip of the spear for such technology would play out in areas such as medicine or general computer system markets, the AI revolution is already underway in the financial markets. Nearly all market-making is presently dominated by machines that employ AI techniques, including advanced pattern recognition. The markets evolution from the primordial ooze of computers, networks and massive storage systems to a complex, intelligent and somewhat singular market entity will impact society well beyond finance.

Investors poured a record high 1.05B into artificial intelligence startups in Q2'16, and AI is already affecting more areas of our lives than many people realize. Even retail and e-commerce companies are increasingly integrating the technology. Recently there's been a rush of AI announcements and acquisitions by major retailers: Just this week, Etsy acquired Blackbird to enhance its search functionality through AI, followed the very next day by Amazon acquiring Angel.ai And earlier this month, e-commerce unicorn Houzz (see our full unicorn tracker here) announced a deep learning initiative to help users find and buy products by clicking on images. Using CB Insights data, we dove into the wide array of AI startups focused on retailers and e-commerce businesses, including AI-powered personal shopping apps, natural language processing and image recognition tools for shopping websites, predictive inventory allocation tools, and more.

A few months ago, my aunt sent her colleagues an email with the subject, "Math Problem! She thought her solution was obvious. Her colleagues, though, were sure their solution was correct--and the two didn't match. Was the problem with one of their answers, or with the puzzle itself? My aunt and her colleagues had stumbled across a fundamental problem in machine learning, the study of computers that learn.

Trying to figure out if the photo your dad emailed you is a porpoise or a nude selfie? Isitnude, the purpose of which ought to be self-explanatory, comes from Algorithmia, a coding company that built it for a client needing a child-friendly site. Algorithmia's CTO, Kenny Daniel, told Wired's Brian Barrett that, as with many goofy online undertakings, it filled a real need that wasn't covered by common sense. "A customer came to us trying to run a site that needs to be kid-friendly," he said. "We'd done quite a bit of work on image recognition. One of our engineers took some algorithms off the shelf, things like image detection, skin color detection, and then put them together."